PROJECT 3: SUMMARY Statins are the frontline treatment for reducing risk of atherosclerotic cardiovascular disease (CVD) and do so primarily by lowering LDL cholesterol (LDLC). The goal of this project is to identify genetic and environmental determinants that mediate the effect of statins on improving lipid levels and preventing adverse CVD-related outcomes. In addition, factors that underlie risk for adverse side effects of statin treatment for some individuals, specifically myopathy and type 2 diabetes mellitus, will be identified. This population-based research will utilize electronic health records (EHR) from which clinical data will be extracted and linked to genetic information as a cost-efficient means to integrate molecular, clinical and environmental data. The study will utilize the Kaiser Permanente (KP) Research Program on Genes, Environment and Health (RPGEH) Genetic Epidemiology Research on Aging (GERA) Cohort. This multi-ethnic cohort contains over 100,000 individuals with full genome-wide data on over 674,000 SNP markers. Demographic, socioeconomic and health-related behaviors are available from participant surveys. KPNC maintains extensive EHR data, for example the typical cohort member has nearly 10 lipid measurements over a two-decade period (1 million measures for the entire cohort) for each of the major lipid fractions, enabling longitudinal and prospective analyses. In addition, the KPNC database contains full information on pharmacy prescriptions and utilization. Specifically, the GERA cohort contains over 46,000 individuals with statin prescriptions. For this subgroup, there are approximately 500,000 lipid panels (half prior and half post statin prescription), 1,300 cases of MACE (major adverse coronary event) and 2,000 cases of ischemic stroke, over 200,000 laboratory test results for creatine kinase (a biomarker for myopathy) and over 500,000 fasting glucose measures (a biomarker for new onset diabetes). Thus, the GERA dataset represents the largest available that links genome-wide genetic data with clinical outcomes in statin users, allowing for evaluation of potential racial/ethnic and sex differences in drug response. Identification of both common and rare variants genetic variants and environmental factors associated with statin clinical efficacy and adverse effects will be assessed in Aim 1 using multiple linear regression and survival analysis time to event with candidate factors included as covariates with interactions. Correlations in closely related individuals and kinship analysis in unrelated individuals will be used to estimate overall heritability of the various efficacy and adverse effect traits (Aim 2). Finally, results will be followed up using a custom exome genotyping array run on 3,360 individuals with target outcomes of interest (Aim 3). Merging together the lipid, biomarker and clinical outcome data with the genome-wide genotype and environmental data on this large cohort will enable novel powerful analyses of genetic and environmental determinants of statin efficacy and side effects. The results from this study will help enable precision medicine, whereby individuals who are most likely to benefit from statin use can be determined, and those most at risk of adverse side effects identified.